MaCuDE IS Task Force Phase II Report: Views of Industry Leaders on Big Data Analytics and AI

2023 | Communications of the Association for Information Systems | Citations: 2

Authors: Lyytinen, Kalle; Topi, Heikki; Tang, Jing

Abstract: This paper represents the Phase II report of the Management Curriculum for the ... Expand

Abstract: This paper represents the Phase II report of the Management Curriculum for the Digital Era (MaCuDE) disciplinary task force on information systems (IS). Aligned with the current work of the AIS (Association for Information Systems) and ACM (Association for Computing Machinery), we focus on the current and future industry driven educational needs and requirements posed by big data analytics (BDA), artificial intelligence (AI), machine learning (ML), and related innovations. In this report, we probe and report on the views of industry leaders regarding BDA/AI education needs. We conducted 18 rich semi-structured interviews with a representative sample of industry leaders situated around key changes and issues related to workforce demands in digital transformation and associated educational needs. We performed a grounded theory based analysis of key themes in reported education needs. We note the shifting meaning of AI and BDA phenomena and identify three main organizational level needs for the digital eracapability improvement and transformation, decision-making strategies and tactics, and changes in operations or products -and connect them to three individual professional competencies necessary to deliver them: fundamental environmental competencies, data information and content, and system design competencies. Based on the analysis we outline several novel competency-based IS curriculum recommendations for the master's and undergraduate level IS education.Architecting for cloud: "And so the thing I noticed, a lot of these companies, when their data is growing so big and their database costs are running so high, they think about going towards Open Source Software, Open Source databases to store all the data and, hence, that creates a demand on the workforce to actually know how to use those. "Understanding data structures, architectures, and governance: "So that's his role, and the key requirement for that role again is Data Architecture, data Engineering and Data Management" Building and managing machine learning models: "Second, of course, is strong competency in the use of Machine Learning packages or models. This field is evolving aggressively." "For developing or programming our own AI solutions, our employees have no deep knowledge of mathematical models and how to build mature and value-creating AI applications." IS management and operations: "The talent sort of wars out there are really, really tough, and finding people who understand that, (how to) generate the data, use the data, think about the data, manipulate it are really, really important. I agree. It used to be you had to have people with Project Management. That was critical and that was everyone competed for that, and now it's that plus the data, the Machine Learning."Based on the data collected in this study, we cannot offer a clear answer to the questions of how BDA and AI affect areas that do not belong to the two main areas of the study. Given, however, how quickly BDA and AI have grown and how substantial related resource requirements are in organizations, it is clear that they already have an impact on several competency needs such as programming language selection, selection of BDA and AI tools based on cloud-based solutions, data architecture, data governance, service repertoires available on platform environments, and special characteristic of BDA and AI projects and how they affect education for project management needs and competencies. Summary of Findings Related to Individual CompetenciesGenerally, we observe a strong alignment and continuity between the recent information systems curriculum recommendations and the findings of this study. The data highlights the continued importance of IS core topics covered in all past curricula, such as data management, systems development, IT infrastructure and architecture (including security), and IS (project) management.The results also highlight also multiple areas that need more emphasis and careful design decisions when schools plan and implement their BDA and AI based programs: Integration of business and technology competencies in ways that allow seamless communication with both business and technology experts  A stronger emphasis on multiple life cycle models and concepts related to data management and data science than in the recent model curricula focused on system life cycles  Heightened focus on new dimensions of data analytics competencies (including communication-related elements such a storytelling and visualization)  The growing importance of ethics as a foundation for ensuring privacy and security of BDA and AI models  Need for more skills in statistics  Understanding the novel requirements that AI solutions set up for IT infrastructure  The importance of competencies in AI systems development and deployment. Collapse

Topics: artificial intelligence big data analytical information system data management decision making

Methods: qualitative interview machine learning meta learning field study personal interview

It's Time to Smarten Up! - A Framework for Building Smart Service Systems

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Wache, Hendrik

Abstract: Smart service systems are a network of people, technologies, as well as process ... Expand

Abstract: Smart service systems are a network of people, technologies, as well as processes and rely on learning, adaptivity, and decision-making to provide smart services to their users. The complexity of designing smart service systems results in a plethora of potential implementation obstacles, often leading to an overly technical focus and resulting in isolated solutions. There is, therefore, a need for a guideline for companies that helps them facilitate the development of complex smart service systems. To this end, we apply action design research used in a multi-year design study for digital twins in the manufacturing industry. The resulting four-quadrant framework (domain, system design, organization, and ecosystem) can help researchers and companies guide the development of smart service systems or evaluate past progress. The evaluation of the framework shows that there might be dominant quadrants and certain paths in the framework, which we would like to investigate in further research. KeywordsSmart service system, digital twin, industry 4.0, action design research. Collapse

Topics: production management digital twin business model systems development mobile system

Methods: action design research group interview personal interview design principle design science

Assimilation of Flexible Information Technologies

2022 | International Conference on Information Systems | Citations: 0

Authors: Barkhordari, Raheleh; Gallupe, Brent

Abstract: This research focuses on the process of how flexible information technologies a ... Expand

Abstract: This research focuses on the process of how flexible information technologies assimilate into work processes and become routinized in an organization's activities. The success of this post-implementation phase is essential if organizations are to reap the benefits of their investments in these technologies. However, many organizations struggle to integrate them with their processes and consequently, do not fully realize the benefits and value of those technologies. We attempt to study IS assimilation using the grounded theory approach to respond to the call for researchers to take the sociomaterial nature of the IS phenomenon into account. To develop a better understanding of the sociomaterial findings of this research, we view these findings through the lens of the Imbrication concept in order to better explain how technology assimilates into organizational practices through gradual interactions of technological and social elements. Several implications for research and practice are discussed. Collapse

Topics: systems development life cycle data visualization

Methods: grounded theory qualitative interview qualitative coding constant comparative analysis case study

Theories: sociomaterialism theory

Toward an Understanding of How Post- Deployment User- Developer Interactions Influence System Utilization

2022 | ACM SIGMIS Database | Citations: 0

Authors: Carraher-Wolverton, Colleen; Burleson, Jim

Abstract: Although initial adoption of an information system has been shown to influence s ... Expand

Abstract: Although initial adoption of an information system has been shown to influence system success, further value can be obtained when end-users move beyond adoption, utilizing more features of the system and integrating it into their work routines. Organizations can increase the post-deployment utilization of their systems by emphasizing continued interaction between developers and end-users. In this study, we develop a research model investigating the influence of shared understanding, faithfulness of appropriation, and consensus on spirit on post-deployment system utilization. Using a sample from a healthcare organization, we show that increased end-user postdeployment interaction with developers supports a shared understanding between the two groups, which ultimately impacts both the routinization and infusion of a system. This study provides a contribution by demonstrating the impact of developer/user interaction in the post-implementation phase of systems development. Collapse

Topics: information system use systems implementation outsourcing participatory design IT investment

Methods: survey mediation analysis design methodology partial least squares regression survey design

Theories: theory of shared mental models structuration theory adaptive structuration theory

Cluster Analysis in Online Learning Communities: A Text Mining Approach

2022 | Communications of the Association for Information Systems | Citations: 6

Authors: Eryilmaz, Evren; Thoms, Brian; Ahmed, Zafor

Abstract: This paper presents a theory-informed blueprint for mining unstructured text da ... Expand

Abstract: This paper presents a theory-informed blueprint for mining unstructured text data using mixed-and multi-methods to improve understanding of collaboration in asynchronous online discussions (AOD). Grounded in a community of inquiry theoretical framework to systematically combine established research techniques, we investigated how AOD topics and individual reflections on those topics affect the formation of clusters or groups in a community. The data for the investigation came from 54 participants and 470 messages. Data analysis combined the analytical efficiency and scalability of topic modeling, social network analysis, and cluster analysis with qualitative content analysis. The cluster analysis found three clusters and that members of the intermediate cluster (i.e., middle of three clusters) played a pivotal role in this community by expressing uncertainty statements, which facilitated a collective sense-making process to resolve misunderstandings. Furthermore, we found that participants' selected discussion topics and how they discussed those topics influenced cluster formations. Theoretical, practical, and methodological implications are discussed in depth. Collapse

Topics: social network online learning system open source social presence big data

Methods: topic model mixed method cluster analysis sociometry qualitative content analysis

How Inconsistencies Between Multiple Conceptual Modeling Scripts Affect Readers’ Understanding

2022 | Americas Conference on Information Systems | Citations: 0

Authors: Jabbari, Mohammad; Werder, Karl

Abstract: IS professionals often use multiple conceptual modeling scripts to develop an u ... Expand

Abstract: IS professionals often use multiple conceptual modeling scripts to develop an understanding of a domain. However, using multiple scripts introduces potential inconsistencies between scrips which can reduce script readers' cognitive ability to develop an understanding. While there are computational methods to avoid or detect inconsistencies, there is a lack of studies on how individuals deal with inconsistencies when they are performing different tasks. We developed a 2x2 between-subject experimental design to investigate the effects of syntactic vs semantic inconsistency on two different systems analysis and design tasks. We expect to contribute to conceptual modeling research, by investigating the effect of inconsistencies, comparing the effects of two tasks, and by elaborating the on role of a pragmatic factor, domain familiarity. Collapse

Topics: task productivity systems development problem solving systems development life cycle accounting

Methods: design science class diagram activity diagram experimental design conceptual modelling

How Do Individuals Understand Multiple Conceptual Modeling Scripts?

2022 | Journal of the Association for Information Systems | Citations: 5

Authors: Jabbari, Mohammad; Recker, Jan; Green, Peter; Werder, Karl

Abstract: Because most real-world domains intended to be supported by an information syst ... Expand

Abstract: Because most real-world domains intended to be supported by an information system are complex, practitioners often use multiple different types of conceptual modeling scripts to understand them. We performed two experiments to examine how two theoretical factors of multiple scriptscombined ontological completeness and ontological overlap-influence how users develop an understanding of a real-world domain from multiple scripts. Results of the first experiment show that to some degree, ontological overlap improves participants' understanding of a domain, more so than combined ontological completeness. In the second experiment, we tracked the eye movement data of participants to understand how ontological overlap between scripts impacts users' information search and cognitive integration processes. We found that some occurrence of semantically similar constructs between scripts helps individuals to identify and relate constructs presented in different scripts. Users, therefore, can identify and focus on script areas that are relevant to their problem tasks. However, a high level of ontological overlap decreases the attention paid by participants to relevant task-specific areas because they spend more time searching for relevant information. Together, our findings both refine and extend existing conceptual modeling theory. We clarify the dialectics between the full and parsimonious real-world representations offered through multiple scripts and the individual's understanding of the domain that is represented by those scripts. Collapse

Topics: problem solving task productivity systems development IS education information storage and retrieval system

Methods: experiment design science conceptual modelling unified modeling language class diagram

Theories: theory of combined ontological completeness and overlap representation theory

Design Knowledge for the Lifecycle Management of Conversational Agents

2022 | International Conference on Business Informatics | Citations: 0

Authors: Lewandowski, Tom; Heuer, Marvin; Vogel, Pascal; Böhmann, Tilo

Abstract: Organizations spend extensive resources on artificial intelligence (AI) solution ... Expand

Abstract: Organizations spend extensive resources on artificial intelligence (AI) solutions in customer service in order to remain customer-focused and competitive. A rising language-based application of AI emerges in the context of conversational agents (CAs), such as chatbots, which represent increasingly intelligent, autonomous, scalable, and cost-effective service platforms. However, AI-based CAs bring new organizational challenges. They are underrepresented in current research, leading to many unanswered questions and research potential regarding the management of their introduction, operation, and improvement. To address this issue, we provide design knowledge that considers the organizational perspective of CAs. Therefore, we conducted a systematic literature review (SLR) and qualitative interview study to reveal and analyze individual issues and challenges, develop meta-requirements, and finally, use them to create design principles. We contribute to the emerging field of CAs that has previously focused mainly on the individual, behavioral, interactional, or technical design. Collapse

Topics: artificial intelligence customer service electronic commerce data management IS management

Methods: qualitative interview design requirement design artifact qualitative content analysis structured literature research

Rigorous Validation of Systems Security Engineering Analytics

2022 | HICSS | Citations: 0

Authors: Llanso, Thomas; Mcneil, Martha; Jamieson, Jessie

Abstract: In response to the asymmetric advantage that attackers enjoy over defenders in c ... Expand

Abstract: In response to the asymmetric advantage that attackers enjoy over defenders in cyber systems, the cyber community has generated a steady stream of cybersecurity-related frameworks, methodologies, analytics, and “best practices” lists. However, these artifacts almost never undergo rigorous validation of their efficacy but instead tend to be accepted on faith, to, we suggest, our collective detriment based on evidence of continued attacker success. But what would rigorous validation look like, and can we afford it? This paper describes the design and estimates the cost of a controlled experiment whose goal is to determine the effectiveness of an exemplar systems security analytic. Given the significant footprint that humans play in cyber systems (e.g., their design, use, attack, and defense), any such experiment must necessarily take into account and control for variable human behavior. Thus, the paper reinforces the argument that cybersecurity can be understood as a hybrid discipline with strong technical and human dimensions. Collapse

Topics: IT security enterprise information system IT security threat systems development life cycle

Methods: experiment laboratory experiment statistical hypothesis test factorial experiment statistical power analysis

Conceptual Foundations on Debiasing for Machine Learning-Based Software

2022 | International Conference on Information Systems | Citations: 0

Authors: Schmitt, Anuschka; Walser, Maximilian; Fahse, Tobias

Abstract: Machine learning (ML)-based software's deployment has raised serious concerns a ... Expand

Abstract: Machine learning (ML)-based software's deployment has raised serious concerns about its pervasive and harmful consequences for users, business, and society inflicted through bias. While approaches to address bias are increasingly recognized and developed, our understanding of debiasing remains nascent. Research has yet to provide a comprehensive coverage of this vast growing field, much of which is not embedded in theoretical understanding. Conceptualizing and structuring the nature, effect, and implementation of debiasing instruments could provide necessary guidance for practitioners investing in debiasing efforts. We develop a taxonomy that classifies debiasing instrument characteristics into seven key dimensions. We evaluate and refine our taxonomy through nine experts and apply our taxonomy to three actual debiasing instruments, drawing lessons for the design and choice of appropriate instruments. Bridging the gaps between our conceptual understanding of debiasing for ML-based software and its organizational implementation, we discuss contributions and future research. Collapse

Topics: systems development new product development decision making system development method organizational context

Methods: qualitative interview structured literature research computational algorithm machine learning literature sample